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Reproducible Research Using R
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table of contents
  1. About
    1. 0.1 What You’ll Learn
    2. 0.2 What You Should Know First
    3. 0.3 What This Book Does Not Cover
  2. How to Use This Book
    1. 0.4 Chapter Anatomy
    2. 0.5 Code, Data, and Reproducibility
    3. 0.6 Acknowledgments
  3. 1 Getting Started with R
    1. 1.1 Learning Objectives
    2. 1.2 RStudio
    3. 1.3 R as a Calculator
      1. 1.3.1 Basic Math
      2. 1.3.2 Built-in mathematical Functions
    4. 1.4 Creating Variables and Assigning Objects
    5. 1.5 Vectors
      1. 1.5.1 Numeric Vectors
      2. 1.5.2 Character Vectors
      3. 1.5.3 Logical Vectors
      4. 1.5.4 Factors (categorical)
      5. 1.5.5 Indexing (1-based in R!)
      6. 1.5.6 Type Coercion
    6. 1.6 Data Frames
      1. 1.6.1 Creating Your Own Data Frame
      2. 1.6.2 Functions to Explore Datasets
      3. 1.6.3 Working With Columns Within Data Frames
    7. 1.7 Reading & Writing data
    8. 1.8 Packages
    9. 1.9 Getting Comfortable Making Mistakes - Help and Advice
    10. 1.10 Key Takeaways
    11. 1.11 Checklist: Before Moving On
    12. 1.12 Key Functions & Commands
    13. 1.13 💡 Reproducibility Tip:
  4. 2 Introduction to tidyverse
    1. 2.1 Learning Objectives {tidyverse-objectives}
    2. 2.2 Using Packages
      1. 2.2.1 Installing Packages
      2. 2.2.2 Loading Packages
    3. 2.3 Meet the tidyverse
      1. 2.3.1 The Pipe
    4. 2.4 Manipulating Data in tidyverse
      1. 2.4.1 Distinct
      2. 2.4.2 Select
      3. 2.4.3 Filter
      4. 2.4.4 Arrange
      5. 2.4.5 Mutate
      6. 2.4.6 If Else
      7. 2.4.7 Renaming Columns
      8. 2.4.8 Putting them all together
    5. 2.5 Insights Into Our Data
      1. 2.5.1 Count
      2. 2.5.2 Summarizing and Grouping
    6. 2.6 Common Gotchas & Quick Fixes
      1. 2.6.1 = vs ==
      2. 2.6.2 NA-aware math
      3. 2.6.3 Pipe position
      4. 2.6.4 Conflicting function names
    7. 2.7 Key Takeaways
    8. 2.8 Checklist
    9. 2.9 Key Functions & Commands
    10. 2.10 💡 Reproducibility Tip:
  5. 3 Visualizations
    1. 3.1 Introduction
    2. 3.2 Learning Objectives
    3. 3.3 Base R
    4. 3.4 ggplot2
      1. 3.4.1 Basics
      2. 3.4.2 Scatterplot - geom_point()
      3. 3.4.3 Bar Chart (counts) and Column Chart (values)
      4. 3.4.4 Histograms and Density Plots (distribution)
      5. 3.4.5 Boxplot - geom_boxplot()
      6. 3.4.6 Lines (time series) - geom_line()
      7. 3.4.7 Put text on the plot - geom_text()
      8. 3.4.8 Error bars (requires summary stats) - geom_errorbar()
      9. 3.4.9 Reference lines
    5. 3.5 Key Takeaways
    6. 3.6 Checklist
    7. 3.7 ggplot2 Visualization Reference
      1. 3.7.1 Summary of ggplot Geometries
      2. 3.7.2 Summary of other ggplot commands
    8. 3.8 💡 Reproducibility Tip:
  6. 4 Comparing Two Groups: Data Wrangling, Visualization, and t-Tests
    1. 4.1 Introduction
    2. 4.2 Learning Objectives {means-objectives}
    3. 4.3 Creating a Sample Dataset
    4. 4.4 Merging Data
      1. 4.4.1 Binding our data
      2. 4.4.2 Joining Data
      3. 4.4.3 Wide Format
      4. 4.4.4 Long Format (Reverse Demo)
    5. 4.5 Comparing Means
      1. 4.5.1 Calculating the means
      2. 4.5.2 t.test
    6. 4.6 Key Takeaways
    7. 4.7 Checklist
      1. 4.7.1 Data Creation & Import
      2. 4.7.2 Comparing Two Means
    8. 4.8 Key Functions & Commands
    9. 4.9 Example APA-style Write-up
    10. 4.10 💡 Reproducibility Tip:
  7. 5 Comparing Multiple Means
    1. 5.1 Introduction
    2. 5.2 Learning Objectives {anova-objectives}
    3. 5.3 Creating Our Data
    4. 5.4 Descriptive Statistics
    5. 5.5 Visualizing Relationships
    6. 5.6 Running a T.Test
    7. 5.7 One-Way ANOVA
    8. 5.8 Post-hoc Tests
    9. 5.9 Adding a Second Factor
    10. 5.10 Model Comparison With AIC
    11. 5.11 Key Takeaways
    12. 5.12 Checklist
    13. 5.13 Key Functions & Commands
    14. 5.14 Example APA-style Write-up
    15. 5.15 💡 Reproducibility Tip:
  8. 6 Analyzing Categorical Data
    1. 6.1 Introduction
    2. 6.2 Learning Objectives {cat-objectives}
    3. 6.3 Loading Our Data
    4. 6.4 Contingency Tables
    5. 6.5 Visualizations
    6. 6.6 Chi-Square Test
    7. 6.7 Cross Tables
    8. 6.8 Contribution
    9. 6.9 CramerV
    10. 6.10 Interpretation
    11. 6.11 Key Takeaways
    12. 6.12 Checklist
    13. 6.13 Key Functions & Commands
    14. 6.14 Example APA-style Write-up
    15. 6.15 💡 Reproducibility Tip:
  9. 7 Correlation
    1. 7.1 Introduction
      1. 7.1.1 Learning Objectives
    2. 7.2 Loading Our Data
    3. 7.3 Cleaning our data
    4. 7.4 Visualizing Relationships
    5. 7.5 Running Correlations (r)
    6. 7.6 Correlation Matrix
    7. 7.7 Coefficient of Determination (R^2)
    8. 7.8 Partial Correlations
    9. 7.9 Biserial and Point-Biserial Correlations
    10. 7.10 Grouped Correlations
    11. 7.11 Conclusion
    12. 7.12 Key Takeaways
    13. 7.13 Checklist
    14. 7.14 Key Functions & Commands
    15. 7.15 Example APA-style Write-up
      1. 7.15.1 Bivariate Correlation
      2. 7.15.2 Positive Correlation
      3. 7.15.3 Partial Correlation
    16. 7.16 💡 Reproducibility Tip:
  10. 8 Linear Regression
    1. 8.1 Introduction
    2. 8.2 Learning Objectives {lin-reg-objectives}
    3. 8.3 Loading Our Data
    4. 8.4 Cleaning Our Data
    5. 8.5 Visualizing Relationships
    6. 8.6 Understanding Correlation
    7. 8.7 Linear Regression Model
    8. 8.8 Checking the residuals
    9. 8.9 Adding more variables
      1. 8.9.1 Bonus code
    10. 8.10 Conclusion
    11. 8.11 Key Takeaways
    12. 8.12 Checklist
    13. 8.13 Key Functions & Commands
    14. 8.14 Example APA-style Write-up
    15. 8.15 💡 Reproducibility Tip:
  11. 9 Logistic Regression
    1. 9.1 Introduction
    2. 9.2 Learning Objectives {log-reg-objectives}
    3. 9.3 Load and Preview Data
    4. 9.4 Exploratory Data Analysis
    5. 9.5 Visualize Relationships
    6. 9.6 Train and Test Split
    7. 9.7 Build Logistic Regression Model
      1. 9.7.1 McFadden’s Pseudo-R²
      2. 9.7.2 Variable Importance
      3. 9.7.3 Multicollinearity check
    8. 9.8 Make Predictions
    9. 9.9 Evaluate Model
    10. 9.10 ROC Curve + AUC
    11. 9.11 Interpretation
    12. 9.12 Key Takeaways
    13. 9.13 Checklist
    14. 9.14 Key Functions & Commands
    15. 9.15 Example APA-style Write-up
    16. 9.16 💡 Reproducibility Tip:
  12. 10 Reproducible Reporting
    1. 10.1 Introduction
    2. 10.2 Learning Objectives {r-markdown-objectives}
    3. 10.3 Creating an R Markdown File
    4. 10.4 Parts of an R Markdown File
      1. 10.4.1 The YAML
      2. 10.4.2 Text
      3. 10.4.3 R Chunks
      4. 10.4.4 Sections
    5. 10.5 Knitting an R Markdown File
    6. 10.6 Publishing an R Markdown file
    7. 10.7 Extras
      1. 10.7.1 Links
      2. 10.7.2 Pictures
      3. 10.7.3 Checklists
      4. 10.7.4 Standout sections
      5. 10.7.5 Changing Setting of Specific R Chunks
    8. 10.8 Key Takeaways
    9. 10.9 Checklist
    10. 10.10 Key Functions & Commands
    11. 10.11 Summary of Common R Markdown Syntax
    12. 10.12 💡 Reproducibility Tip:
  13. Appendix: Reproducibility Checklist for Data Analysis in R
    1. 10.13 Project & Environment
    2. 10.14 Data Integrity & Structure
    3. 10.15 Data Transformation & Workflow
    4. 10.16 Merging & Reshaping Data
    5. 10.17 Visualization & Communication
    6. 10.18 Statistical Reasoning
    7. 10.19 Modeling & Inference
    8. 10.20 Randomness & Evaluation
    9. 10.21 Reporting & Execution
    10. 10.22 Final Check
  14. Packages & Functions Reference

About

This book was inspired by my students—particularly graduate students at Brooklyn College who wanted to ask meaningful research questions but felt held back by tools that were opaque, brittle, or difficult to reproduce. Over time, it became clear that learning R was not just about writing code; it was about learning how to think clearly, document decisions, and produce work that others (and your future self) can understand and reuse.

Reproducible Research in R is an open educational resource (OER) designed to help students, researchers, and practitioners build confidence using R as a complete research tool—from data import and visualization to statistical analysis and polished reporting. The goal of this book is not just to teach what buttons to press or which functions to call, but to teach a workflow that is transparent, explainable, and most importantly, reproducible.

This book was created in conjunction with the Open Educational Resources initiative at Brooklyn College and is freely available for learning, teaching, and adaptation.

0.1 What You’ll Learn

By working through this book, you will learn how to:

  • Use R and RStudio as an integrated research environment
  • Import, clean, and explore data using modern R tools
  • Visualize data clearly and intentionally
  • Conduct common statistical analyses used in applied research
  • Interpret results in context—not just report numbers
  • Create fully reproducible reports using R Markdown and Bookdown

Throughout the book, reproducibility is treated not as an “extra” or an advanced topic, but as a default practice.

0.2 What You Should Know First

You do not need prior experience with:

  • R
  • Programming
  • Command-line tools

Basic familiarity with research methods and statistics is helpful, but the focus of this book is on implementation and workflow, not statistical theory.

0.3 What This Book Does Not Cover

This book is not intended to be:

  • A comprehensive statistics theory textbook
  • A software engineering or computer science text
  • An advanced machine learning or big-data resource

Instead, it focuses on the tools and practices most commonly needed to conduct and present reproducible research in applied settings.

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